Patterns and Determinants of Multimorbidity in Older Adults: Study in Health-Ecological Perspective

Yiming Chen, Lei Shi, Xiao Zheng, Juan Yang, Yaqing Xue, Shujuan Xiao, Benli Xue, Jiachi Zhang, Xinru Li, Huang Lin, Chao Ma and Chichen Zhang ()
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Yiming Chen: School of Health Management, Southern Medical University, Guangzhou 510515, China
Lei Shi: School of Health Management, Southern Medical University, Guangzhou 510515, China
Xiao Zheng: Department of Health Management, Shunde Hospital, Southern Medical University, Foshan 528399, China
Juan Yang: School of Health Management, Bengbu Medical College, Bengbu 233030, China
Yaqing Xue: School of Health Management, Southern Medical University, Guangzhou 510515, China
Shujuan Xiao: School of Health Management, Southern Medical University, Guangzhou 510515, China
Benli Xue: School of Health Management, Southern Medical University, Guangzhou 510515, China
Jiachi Zhang: School of Health Management, Southern Medical University, Guangzhou 510515, China
Xinru Li: School of Health Management, Southern Medical University, Guangzhou 510515, China
Huang Lin: School of Health Management, Southern Medical University, Guangzhou 510515, China
Chao Ma: School of Health Management, Southern Medical University, Guangzhou 510515, China
Chichen Zhang: School of Health Management, Southern Medical University, Guangzhou 510515, China

IJERPH, 2022, vol. 19, issue 24, 1-15

Abstract: (1) Background: Multimorbidity has become one of the key issues in the public health sector. This study aims to explore the patterns and health-ecological factors of multimorbidity in China to propose policy recommendations for the management of chronic diseases in the elderly. (2) Methods: A multi-stage random sampling method was used to conduct a questionnaire survey on 3637 older adults aged 60 and older in Shanxi, China. Association rule mining analysis (ARM) and network analysis were applied to analyze the patterns of multimorbidity. The health-ecological model was adopted to explore the potential associated factors of multimorbidity in a multidimensional perspective. A hierarchical multiple logistic model was employed to investigate the association strengths reflected by adjusted odds ratios and 95% confidence. (3) Results: Multimorbidity occurred in 20.95% of the respondents. The graph of network analysis showed that there were 6 combinations of chronic diseases with strong association strengths and 14 with moderate association strengths. The results of the ARM were similar to the network analysis; six dyadic chronic disease combinations and six triadic ones were obtained. Hierarchical multiple logistic regression indicated that innate personal traits (age, history of genetics, and body mass index), behavioral lifestyle (physical activity levels and medication adherence), interpersonal network (marital status), and socioeconomic status (educational level) were the common predictors of multimorbidity for older adults, among which, having no family history was found to be a relative determinant as a protective factor for multimorbidity after controlling the other covariates. (4) Conclusions: multimorbidity was prevalent in older adults and most disease combinations are associated with hypertension, followed by diabetes. This shows that diabetes and hypertension have a high prevalence among older adults and have a wide range of associations with other chronic diseases. Exploring the patterns and associated factors of multimorbidity will help the country prevent complications and avoid the unnecessary use of the health service, adopting an integrated approach to managing multimorbidity rather than an individual disease-specific approach and implementing different strategies according to the location of residence.

Keywords: multimorbidity; older adults; pattern; health-ecological model; health management (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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